OpenRaiser/Intern-Atlas huggingface dataset:
Efficient Dense Frontier Detection for 2D Graph SLAM Based on Occupancy Grid Submaps
Venue: arXiv cs.RO 2019
Year: 2019

In autonomous robot exploration, the frontier is the border in the world map between the explored space and unexplored space. The frontier plays an important role when deciding where in the environment the robots should go explore next. We examine a modular control system pipeline for autonomous exploration where a 2D graph SLAM algorithm based on occupancy grid submaps performs map building and localization. We provide an overview of the state of the art in frontier detection and the relevant SLAM concepts and propose a specialized frontier detection method which is efficiently constrained to active submaps, yet robust to SLAM loop closures.
Predictive Inequity in Object Detection
Venue: arXiv cs.CV 2019
Year: 2019

In this work, we investigate whether state-of-the-art object detection systems have equitable predictive performance on pedestrians with different skin tones. This work is motivated by many recent examples of ML and vision systems displaying higher error rates for certain demographic groups than others. We annotate an existing large scale dataset which contains pedestrians, BDD100K, with Fitzpatrick skin tones in ranges [1-3] or [4-6]. We then provide an in-depth comparative analysis of performance between these two skin tone groupings, finding that neither time of day nor occlusion explain this behavior, suggesting this disparity is not merely the result of pedestrians in the 4-6 range appearing in more difficult scenes for detection. We investigate to what extent time of day, occlusion, and reweighting the supervised loss during training affect this predictive bias.
Operational Neural Networks
Venue: arXiv cs.CV 2019
Year: 2019

Feed-forward, fully-connected Artificial Neural Networks (ANNs) or the so-called Multi-Layer Perceptrons (MLPs) are well-known universal approximators. However, their learning performance varies significantly depending on the function or the solution space that they attempt to approximate. This is mainly because of their homogenous configuration based solely on the linear neuron model. Therefore, while they learn very well those problems with a monotonous, relatively simple and linearly separable solution space, they may entirely fail to do so when the solution space is highly nonlinear and complex. Sharing the same linear neuron model with two additional constraints (local connections and weight sharing), this is also true for the conventional Convolutional Neural Networks (CNNs) and, it is, therefore, not surprising that in many challenging problems only the deep CNNs with a massive complexity and depth can achieve the required diversity and the learning performance. In order to address this drawback and also to accomplish a more generalized model over the convolutional neurons, this study proposes a novel network model, called Operational Neural Networks (ONNs), which can be heterogeneous and encapsulate neurons with any set of operators to boost diversity and to learn highly complex and multi-modal functions or spaces with minimal network complexity and training data. Finally, a novel training method is formulated to back-propagate the error through the operational layers of ONNs. Experimental results over highly challenging problems demonstrate the superior learning capabilities of ONNs even with few neurons and hidden layers.
Diffusive Molecular Communications with Reactive Molecules: Channel Modeling and Signal Design
Venue: arXiv cs.IT 2019
Year: 2019

This paper focuses on molecular communication (MC) systems using two types of signaling molecules which may participate in a reversible bimolecular reaction in the channel. The motivation for studying these MC systems is that they can realize the concept of constructive and destructive signal superposition, which leads to favorable properties such as inter-symbol interference (ISI) reduction and avoiding environmental contamination due to continuous release of signaling molecules into the channel. This work first presents a general formulation for binary modulation schemes that employ two types of signaling molecules and proposes several modulation schemes as special cases. Moreover, two types of receivers are considered: a receiver that is able to observe both types of signaling molecules (2TM), and a simpler receiver that can observe only one type of signaling molecules (1TM). For both of these receivers, the maximum likelihood (ML) detector for general binary modulation is derived under the assumption that the detector has perfect knowledge of the ISI-causing sequence. In addition, two suboptimal detectors of different complexity are proposed, namely an ML-based detector that employs an estimate of the ISI-causing sequence and a detector that neglects the effect of ISI. The proposed detectors, except for the detector that neglects ISI for the 2TM receiver, require knowledge of the channel response (CR) of the considered MC system. Moreover, the CR is needed for performance evaluation of all proposed detectors. However, deriving the CR of MC systems with reactive signaling molecules is challenging since the underlying partial differential equations that describe the reaction-diffusion mechanism are coupled and non-linear. Therefore, we develop an algorithm for efficient computation of the CR and validate its accuracy via particle-based simulation.
On the generalization of GAN image forensics
Venue: arXiv cs.CV 2019
Year: 2019

Recently the GAN generated face images are more and more realistic with high-quality, even hard for human eyes to detect. On the other hand, the forensics community keeps on developing methods to detect these generated fake images and try to guarantee the credibility of visual contents. Although researchers have developed some methods to detect generated images, few of them explore the important problem of generalization ability of forensics model. As new types of GANs are emerging fast, the generalization ability of forensics models to detect new types of GAN images is absolutely an essential research topic. In this paper, we explore this problem and propose to use preprocessed images to train a forensic CNN model. By applying similar image level preprocessing to both real and fake training images, the forensics model is forced to learn more intrinsic features to classify the generated and real face images. Our experimental results also prove the effectiveness of the proposed method.
The FAIR Funder pilot programme to make it easy for funders to require and for grantees to produce FAIR Data
Venue: arXiv cs.DL 2019
Year: 2019

There is a growing acknowledgement in the scientific community of the importance of making experimental data machine findable, accessible, interoperable, and reusable (FAIR). Recognizing that high quality metadata are essential to make datasets FAIR, members of the GO FAIR Initiative and the Research Data Alliance (RDA) have initiated a series of workshops to encourage the creation of Metadata for Machines (M4M), enabling any self-identified stakeholder to define and promote the reuse of standardized, comprehensive machine-actionable metadata. The funders of scientific research recognize that they have an important role to play in ensuring that experimental results are FAIR, and that high quality metadata and careful planning for FAIR data stewardship are central to these goals. We describe the outcome of a recent M4M workshop that has led to a pilot programme involving two national science funders, the Health Research Board of Ireland (HRB) and the Netherlands Organisation for Health Research and Development (ZonMW). These funding organizations will explore new technologies to define at the time that a request for proposals is issued the minimal set of machine-actionable metadata that they would like investigators to use to annotate their datasets, to enable investigators to create such metadata to help make their data FAIR, and to develop data-stewardship plans that ensure that experimental data will be managed appropriately abiding by the FAIR principles. The FAIR Funders design envisions a data-management workflow having seven essential stages, where solution providers are openly invited to participate. The initial pilot programme will launch using existing computer-based tools of those who attended the M4M Workshop.
Enhanced Security of Symmetric Encryption Using Combination of Steganography with Visual Cryptography
Venue: arXiv cs.CR 2019
Year: 2019

Data security is required when communications over untrusted networks takes place. Security tools such as cryptography and steganography are applied to achieve such objectives, but both have limitations and susceptible to attacks if they were used individually. To overcome these limitations, we proposed a powerful and secured system based on the integration of cryptography and steganography. The secret message is encrypted with blowfish cipher and visual cryptography. Finally, the encrypted data is embedded into two innocent cover images for future transmission. An extended analysis was made to prove the efficiency of the proposed model by measuring Mean-Square-Error (MSE), Peak-Signal-to-noise-Ratio (PSNR), and image histogram. The robustness was examined by launching statistical and 8-bit plane visual attacks. The proposed model provides a secure mean to transmit or store highly classified data that could be applied to the public security sector.
Como Mensurar a Importância, Influência e a Relevância de Usuários do Twitter? Uma análise da interação dos candidatos à presidência do Brasil nas eleições de 2018
Venue: arXiv cs.DL 2019
Year: 2019

In the contemporary world, a significant number of people use social networking services for a variety of purposes, including, but not limited to, communicating, exchanging messages and searching for information. A popular social network in the political arena is Twitter, a microblogging service for posting messages of up to 280 characters, called "tweets," where influential politicians from various countries often use this medium to spread ideas and make public statements. In this work, an analysis was made of the connections of candidates for the presidency of the Republic of Brazil in the year 2018. Using the analysis of complex networks to measure influence and relevance, a metric was established able to quantify the importance of users in the network. As part of the analysis, a Memory Algorithm was used to detect communities, groups of strongly connected vertices (tweets) evidencing groupings of users.
B-Script: Transcript-based B-roll Video Editing with Recommendations
Venue: arXiv cs.HC 2019
Year: 2019

In video production, inserting B-roll is a widely used technique to enrich the story and make a video more engaging. However, determining the right content and positions of B-roll and actually inserting it within the main footage can be challenging, and novice producers often struggle to get both timing and content right. We present B-Script, a system that supports B-roll video editing via interactive transcripts. B-Script has a built-in recommendation system trained on expert-annotated data, recommending users B-roll position and content. To evaluate the system, we conducted a within-subject user study with 110 participants, and compared three interface variations: a timeline-based editor, a transcript-based editor, and a transcript-based editor with recommendations. Users found it easier and were faster to insert B-roll using the transcript-based interface, and they created more engaging videos when recommendations were provided.
Scalability Aspects of Cell-Free Massive MIMO
Venue: arXiv cs.IT 2019
Year: 2019

Ubiquitous cell-free massive MIMO (multiple-input multiple-output) combines massive MIMO technology and user-centric transmission in a distributed architecture. All the access points (APs) in the network cooperate to jointly and coherently serve a smaller number of users in the same time-frequency resource. However, this coordination needs significant amounts of control signalling which introduces additional overhead, while data co-processing increases the back/front-haul requirements. Hence, the notion that the "whole world" could constitute one network, and that all APs would act as a single base station, is not scalable. In this study, we address some system scalability aspects of cell-free massive MIMO that have been neglected in literature until now. In particular, we propose and evaluate a solution related to data processing, network topology and power control. Results indicate that our proposed framework achieves full scalability at the cost of a modest performance loss compared to the canonical form of cell-free massive MIMO.
High Degree Vertices and Spread of Infections in Spatially Modelled Social Networks
Venue: arXiv cs.SI 2019
Year: 2019

We examine how the behaviour of high degree vertices in a network affects whether an infection spreads through communities or jumps between them. We study two stochastic susceptible-infected-recovered (SIR) processes and represent our network with a spatial preferential attachment (SPA) network. In one of the two epidemic scenarios we adjust the contagiousness of high degree vertices so that they are less contagious. We show that, for this scenario, the infection travels through communities rather than jumps between them. We conjecture that this is not the case in the other scenario, when contagion is independent of the degree of the originating vertex. Our theoretical results and conjecture are supported by simulations.
Broad Neural Network for Change Detection in Aerial Images
Venue: arXiv cs.CV 2019
Year: 2019

A change detection system takes as input two images of a region captured at two different times, and predicts which pixels in the region have undergone change over the time period. Since pixel-based analysis can be erroneous due to noise, illumination difference and other factors, contextual information is usually used to determine the class of a pixel (changed or not). This contextual information is taken into account by considering a pixel of the difference image along with its neighborhood. With the help of ground truth information, the labeled patterns are generated. Finally, Broad Learning classifier is used to get prediction about the class of each pixel. Results show that Broad Learning can classify the data set with a significantly higher F-Score than that of Multilayer Perceptron. Performance comparison has also been made with other popular classifiers, namely Multilayer Perceptron and Random Forest.
GAN Based Image Deblurring Using Dark Channel Prior
Venue: arXiv cs.CV 2019
Year: 2019

A conditional general adversarial network (GAN) is proposed for image deblurring problem. It is tailored for image deblurring instead of just applying GAN on the deblurring problem. Motivated by that, dark channel prior is carefully picked to be incorporated into the loss function for network training. To make it more compatible with neuron networks, its original indifferentiable form is discarded and L2 norm is adopted instead. On both synthetic datasets and noisy natural images, the proposed network shows improved deblurring performance and robustness to image noise qualitatively and quantitatively. Additionally, compared to the existing end-to-end deblurring networks, our network structure is light-weight, which ensures less training and testing time.
A Method Beyond Channel Capacity in the Low SNR Regime: Theoretical Proof and Numerical Confirmation
Venue: arXiv cs.IT 2019
Year: 2019

A method is proposed, referred to as the cocktail BPSK, to use two independent BPSK streams layered in a parallel transmission through the additive white Gaussian channel. The critical point of the method is to translate the interference between the two layered symbols into a problem of one symbol with two possible amplitudes, whereby we find that the achievable data rate (ADR) of the first layer equals to the channel capacity at zero signal-to-noise-ratio limit. By adding the contribution of the second layer, we find that the total ADR of the two layers can be larger than the channel capacity. The theoretical proof is done based on the mutual information and the numerical results are added for the confirmation.
Image-Based Geo-Localization Using Satellite Imagery
Venue: arXiv cs.CV 2019
Year: 2019

The problem of localization on a geo-referenced satellite map given a query ground view image is useful yet remains challenging due to the drastic change in viewpoint. To this end, in this paper we work on the extension of our earlier work on the Cross-View Matching Network (CVM-Net) for the ground-to-aerial image matching task since the traditional image descriptors fail due to the drastic viewpoint change. In particular, we show more extensive experimental results and analyses of the network architecture on our CVM-Net. Furthermore, we propose a Markov localization framework that enforces the temporal consistency between image frames to enhance the geo-localization results in the case where a video stream of ground view images is available. Experimental results show that our proposed Markov localization framework can continuously localize the vehicle within a small error on our Singapore dataset.
Distributed Variational Bayesian Algorithms for Extended Object Tracking
Venue: arXiv cs.SY 2019
Year: 2019

This paper is concerned with the problem of distributed extended object tracking, which aims to collaboratively estimate the state and extension of an object by a network of nodes. In traditional tracking applications, most approaches consider an object as a point source of measurements due to limited sensor resolution capabilities. Recently, some studies consider the extended objects, which are spatially structured, i.e., multiple resolution cells are occupied by an object. In this setting, multiple measurements are generated by each object per time step. In this paper, we present a Bayesian model for extended object tracking problem in a sensor network. In this model, the object extension is represented by a symmetric positive definite random matrix, and we assume that the measurement noise exists but is unknown. Using this Bayesian model, we first propose a novel centralized algorithm for extended object tracking based on variational Bayesian methods. Then, we extend it to the distributed scenario based on the alternating direction method of multipliers (ADMM) technique. The proposed algorithms can simultaneously estimate the extended object state (the kinematic state and extension) and the measurement noise covariance. Simulations on both extended object tracking and group target tracking are given to verify the effectiveness of the proposed model and algorithms.
MIPS-Core Application Specific Instruction-Set Processor for IDEA Cryptography - Comparison between Single-Cycle and Multi-Cycle Architectures
Venue: arXiv cs.AR 2019
Year: 2019

A single-cycle processor completes the execution of an instruction in only one clock cycle. However, its clock period is usually rather long. On the contrary, although clock frequency is higher in a multi-cycle processor, it takes several clock cycles to finish an instruction. Therefore, their runtime efficiencies depend on which program is executed. This paper presents a new processor for International Data Encryption Algorithm (IDEA) cryptography. The new design is an Application Specific Instruction-set Processor (ASIP) in which both general-purpose and special instructions are supported. It is a single-cycle MIPS-core architecture, whose average Clocks Per Instruction (CPI) is 1. Furthermore, a comparison is provided in this paper to show the differences between the proposed single-cycle processor and another comparable multi-cycle crypto processor. FPGA implementation results show that both architectures have almost the same encoding/decoding throughput. However, the previous processor consumes nearly twice as many resources as the new one does.
KT-Speech-Crawler: Automatic Dataset Construction for Speech Recognition from YouTube Videos
Venue: arXiv cs.CL 2019
Year: 2019

In this paper, we describe KT-Speech-Crawler: an approach for automatic dataset construction for speech recognition by crawling YouTube videos. We outline several filtering and post-processing steps, which extract samples that can be used for training end-to-end neural speech recognition systems. In our experiments, we demonstrate that a single-core version of the crawler can obtain around 150 hours of transcribed speech within a day, containing an estimated 3.5% word error rate in the transcriptions. Automatically collected samples contain reading and spontaneous speech recorded in various conditions including background noise and music, distant microphone recordings, and a variety of accents and reverberation. When training a deep neural network on speech recognition, we observed around 40\% word error rate reduction on the Wall Street Journal dataset by integrating 200 hours of the collected samples into the training set. The demo (http://emnlp-demo.lakomkin.me/) and the crawler code (https://github.com/EgorLakomkin/KTSpeechCrawler) are publicly available.
A Framework for Detecting Event related Sentiments of a Community
Venue: arXiv cs.SI 2019
Year: 2019

Social media has revolutionized human communication and styles of interaction. Due to its easiness and effective medium, people share and exchange information, carry out discussion on various events, and express their opinions. For effective policy making and understanding the response of a community on different events, we need to monitor and analyze the social media. In social media, there are some users who are more influential, for example, a famous politician may have more influence than a common person. These influential users belong to specific communities. The main object of this research is to know the sentiments of a specific community on various events. For detecting the event based sentiments of a community we propose a generic framework. Our framework identifies the users of a specific community on twitter. After identifying the users of a community, we fetch their tweets and identify tweets belonging to specific events. The event based tweets are pre-processed. Pre-processed tweets are then analyzed for detecting sentiments of a community for specific events. Qualitative and quantitative evaluation confirms the effectiveness and usefulness of our proposed framework.
Adversarial Generation of Handwritten Text Images Conditioned on Sequences
Venue: arXiv cs.CV 2019
Year: 2019

State-of-the-art offline handwriting text recognition systems tend to use neural networks and therefore require a large amount of annotated data to be trained. In order to partially satisfy this requirement, we propose a system based on Generative Adversarial Networks (GAN) to produce synthetic images of handwritten words. We use bidirectional LSTM recurrent layers to get an embedding of the word to be rendered, and we feed it to the generator network. We also modify the standard GAN by adding an auxiliary network for text recognition. The system is then trained with a balanced combination of an adversarial loss and a CTC loss. Together, these extensions to GAN enable to control the textual content of the generated word images. We obtain realistic images on both French and Arabic datasets, and we show that integrating these synthetic images into the existing training data of a text recognition system can slightly enhance its performance.